This document contains answers to some of the most frequently asked questions about R.
This document is copyright © 1998–2008 by Kurt Hornik.
This document is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version.
This document is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
A copy of the GNU General Public License is available via WWW at
http://www.gnu.org/copyleft/gpl.html.
You can also obtain it by writing to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, U.S.A.
The latest version of this document is always available from
http://CRAN.R-project.org/doc/FAQ/
From there, you can obtain versions converted to plain ASCII text, DVI, GNU info, HTML, PDF, PostScript as well as the Texinfo source used for creating all these formats using the GNU Texinfo system.
You can also obtain the R FAQ from the doc/FAQ subdirectory of a CRAN site (see What is CRAN?).
In publications, please refer to this FAQ as Hornik (2008), “The R FAQ”, and give the above, official URL and the ISBN 3-900051-08-9:
@Misc{,
author = {Kurt Hornik},
title = {The {R} {FAQ}},
year = {2008},
note = {{ISBN} 3-900051-08-9},
url = {http://CRAN.R-project.org/doc/FAQ/R-FAQ.html}
}
Everything should be pretty standard. ‘R>’ is used for the R prompt, and a ‘$’ for the shell prompt (where applicable).
Feedback via email to Kurt.Hornik@R-project.org is of course most welcome.
In particular, note that I do not have access to Windows or Macintosh systems. Features specific to the Windows and Mac OS X ports of R are described in the “R for Windows FAQ” and the “R for Mac OS X FAQ. If you have information on Macintosh or Windows systems that you think should be added to this document, please let me know.
R is a system for statistical computation and graphics. It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files.
The design of R has been heavily influenced by two existing languages: Becker, Chambers & Wilks' S (see What is S?) and Sussman's Scheme. Whereas the resulting language is very similar in appearance to S, the underlying implementation and semantics are derived from Scheme. See What are the differences between R and S?, for further details.
The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. Most of the user-visible functions in R are written in R. It is possible for the user to interface to procedures written in the C, C++, or FORTRAN languages for efficiency. The R distribution contains functionality for a large number of statistical procedures. Among these are: linear and generalized linear models, nonlinear regression models, time series analysis, classical parametric and nonparametric tests, clustering and smoothing. There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations. Additional modules (“add-on packages”) are available for a variety of specific purposes (see R Add-On Packages).
R was initially written by Ross Ihaka and Robert Gentleman at the Department of Statistics of the University of Auckland in Auckland, New Zealand. In addition, a large group of individuals has contributed to R by sending code and bug reports.
Since mid-1997 there has been a core group (the “R Core Team”) who can modify the R source code archive. The group currently consists of Doug Bates, John Chambers, Peter Dalgaard, Robert Gentleman, Kurt Hornik, Stefano Iacus, Ross Ihaka, Friedrich Leisch, Thomas Lumley, Martin Maechler, Duncan Murdoch, Paul Murrell, Martyn Plummer, Brian Ripley, Duncan Temple Lang, Luke Tierney, and Simon Urbanek.
R has a home page at http://www.R-project.org/. It is free software distributed under a GNU-style copyleft, and an official part of the GNU project (“GNU S”).
R is being developed for the Unix, Windows and Mac families of operating systems. Support for Mac OS Classic ended with R 1.7.1.
The current version of R will configure and build under a number of common Unix platforms including cpu-linux-gnu for the i386, alpha, arm, hppa, ia64, m68k, mips/mipsel, powerpc, s390, sparc (e.g., http://buildd.debian.org/build.php?&pkg=r-base), and x86_64 CPUs, powerpc-apple-darwin, mips-sgi-irix, rs6000-ibm-aix, and sparc-sun-solaris.
If you know about other platforms, please drop us a note.
The current released version is 2.7.0. Based on this `major.minor.patchlevel' numbering scheme, there are two development versions of R, a patched version of the current release (`r-patched') and one working towards the next minor or eventually major (`r-devel') releases of R, respectively. Version r-patched is for bug fixes mostly. New features are typically introduced in r-devel.
Sources, binaries and documentation for R can be obtained via CRAN, the “Comprehensive R Archive Network” (see What is CRAN?).
Sources are also available via https://svn.R-project.org/R/, the R Subversion repository, but currently not via anonymous rsync (nor CVS).
Tarballs with daily snapshots of the r-devel and r-patched development versions of R can be found at ftp://ftp.stat.math.ethz.ch/Software/R.
If R is already installed, it can be started by typing R at the shell prompt (of course, provided that the executable is in your path).
If binaries are available for your platform (see Are there Unix binaries for R?), you can use these, following the instructions that come with them.
Otherwise, you can compile and install R yourself, which can be done very easily under a number of common Unix platforms (see What machines does R run on?). The file INSTALL that comes with the R distribution contains a brief introduction, and the “R Installation and Administration” guide (see What documentation exists for R?) has full details.
Note that you need a FORTRAN compiler or perhaps f2c in addition to a C compiler to build R. Also, you need Perl version 5 to build the R object documentations. (If this is not available on your system, you can obtain a PDF version of the object reference manual via CRAN.)
In the simplest case, untar the R source code, change to the directory thus created, and issue the following commands (at the shell prompt):
$ ./configure
$ make
If these commands execute successfully, the R binary and a shell script front-end called R are created and copied to the bin directory. You can copy the script to a place where users can invoke it, for example to /usr/local/bin. In addition, plain text help pages as well as HTML and LaTeX versions of the documentation are built.
Use make dvi to create DVI versions of the R manuals, such as refman.dvi (an R object reference index) and R-exts.dvi, the “R Extension Writers Guide”, in the doc/manual subdirectory. These files can be previewed and printed using standard programs such as xdvi and dvips. You can also use make pdf to build PDF (Portable Document Format) version of the manuals, and view these using e.g. Acrobat. Manuals written in the GNU Texinfo system can also be converted to info files suitable for reading online with Emacs or stand-alone GNU Info; use make info to create these versions (note that this requires Makeinfo version 4.5).
Finally, use make check to find out whether your R system works correctly.
You can also perform a “system-wide” installation using make install. By default, this will install to the following directories:
In the above, prefix is determined during configuration
(typically /usr/local) and can be set by running
configure with the option
$ ./configure --prefix=/where/you/want/R/to/go
(E.g., the R executable will then be installed into /where/you/want/R/to/go/bin.)
To install DVI, info and PDF versions of the manuals, use make install-dvi, make install-info and make install-pdf, respectively.
The bin/windows directory of a CRAN site contains binaries for a base distribution and a large number of add-on packages from CRAN to run on Windows 95, 98, ME, NT4, 2000, and XP (at least) on Intel and clones (but not on other platforms). The Windows version of R was created by Robert Gentleman and Guido Masarotto, and is now being developed and maintained by Duncan Murdoch and Brian D. Ripley.
For most installations the Windows installer program will be the easiest tool to use.
See the “R for Windows FAQ” for more details.
The bin/macosx directory of a CRAN site contains a standard Apple installer package inside a disk image named R.dmg. Once downloaded and executed, the installer will install the current non-developer release of R. RAqua is a native Mac OS X Darwin version of R with a R.app Mac OS X GUI. Inside bin/macosx/powerpc/contrib/x.y there are prebuilt binary packages (for powerpc version of Mac OS X) to be used with RAqua corresponding to the “x.y” release of R. The installation of these packages is available through the “Package” menu of the R.app GUI. This port of R for Mac OS X is maintained by Stefano Iacus. The “R for Mac OS X FAQ has more details.
The bin/macos directory of a CRAN site contains bin-hexed (hqx) and stuffit (sit) archives for a base distribution and a large number of add-on packages of R 1.7.1 to run under Mac OS 8.6 to Mac OS 9.2.2. This port of R for Macintosh is no longer supported.
The bin/linux directory of a CRAN site contains the following packages.
CPU Versions Provider Debian i386/amd64 stable/oldstable Johannes Ranke Red Hat i386/x86_64 fedora7/fedora8 Martyn Plummer i386/x86_64 el4/el5 Bob Kinney SuSE i586/x86_64 10.0/10.1/10.2/10.3 Detlef Steuer Ubuntu i386 dapper/feisty/gutsy/hardy Vincent Goulet amd64 dapper/feisty/gutsy/hardy Michael Rutter
Debian packages, maintained by Dirk Eddelbuettel and Doug Bates, have
long been part of the Debian distribution, and can be accessed through
APT, the Debian package maintenance tool. Use e.g. apt-get
install r-base r-recommended to install the R environment and
recommended packages. If you also want to build R packages from source,
also run apt-get install r-base-dev to obtain the additional
tools required for this. So-called “backports” of the current R
packages for the stable distribution of Debian are provided by
Johannes Ranke, and available from CRAN. Simply add the line
deb http://CRAN.R-project.org/bin/linux/debian stable/
(feel free to use a CRAN mirror instead of the master) to the file /etc/apt/sources.list, and install as usual. More details on installing and administering R on Debian Linux can be found at http://CRAN.R-project.org/bin/linux/debian/README. These backports should also be suitable for other Debian derivatives. Native backports for Ubuntu are provided by Vincent Goulet and Michael Rutter.
On SUSE, you can set up an installation source for R within Yast by setting (e.g.)
Protocol: HTTP
Server name: software.openSUSE.org
Directory: /download/home:/dsteuer/openSUSE_10.2/
With this setting, online updates will check for new versions of R.
No other binary distributions are currently publically available via CRAN.
A “live” Linux distribution with a particular focus on R is Quantian, which provides a directly bootable and self-configuring “Live DVD” containing numerous applications of interests to scientists and researchers, including several hundred CRAN and Bioconductor packages, the “ESS” extensions for Emacs, the “JGR” Java GUI for R, the Ggobi visualization tool as well as several other R interfaces. The Quantian website at http://dirk.eddelbuettel.com/quantian/ contains more details as well download information.
Online documentation for most of the functions and variables in R exists, and can be printed on-screen by typing help(name) (or ?name) at the R prompt, where name is the name of the topic help is sought for. (In the case of unary and binary operators and control-flow special forms, the name may need to be be quoted.)
This documentation can also be made available as one reference manual for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX, see How can R be installed?. An up-to-date HTML version is always available for web browsing at http://stat.ethz.ch/R-manual/.
Printed copies of the R reference manual for some version(s) are available from Network Theory Ltd, at http://www.network-theory.co.uk/R/base/. For each set of manuals sold, the publisher donates USD 10 to the R Foundation (see What is the R Foundation?).
The R distribution also comes with the following manuals.
Books on R include
P. Dalgaard (2002), “Introductory Statistics with R”, Springer: New York, ISBN 0-387-95475-9, http://www.biostat.ku.dk/~pd/ISwR.html.J. Fox (2002), “An R and S-Plus Companion to Applied Regression”, Sage Publications, ISBN 0-761-92280-6 (softcover) or 0-761-92279-2 (hardcover), http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/.
J. Maindonald and J. Braun (2003), “Data Analysis and Graphics Using R: An Example-Based Approach”, Cambridge University Press, ISBN 0-521-81336-0, http://wwwmaths.anu.edu.au/~johnm/.
S. M. Iacus and G. Masarotto (2002), “Laboratorio di statistica con R”, McGraw-Hill, ISBN 88-386-6084-0 (in Italian),
http://www.ateneonline.it/LibroAteneo.asp?item_id=1436.P. Murrell (2005), “R Graphics”, Chapman & Hall/CRC, ISBN: 1-584-88486-X, http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html.
The book
W. N. Venables and B. D. Ripley (2002), “Modern Applied Statistics with S. Fourth Edition”. Springer, ISBN 0-387-95457-0
has a home page at http://www.stats.ox.ac.uk/pub/MASS4/ providing additional material. Its companion is
W. N. Venables and B. D. Ripley (2000), “S Programming”. Springer, ISBN 0-387-98966-8
and provides an in-depth guide to writing software in the S language which forms the basis of both the commercial S-Plus and the Open Source R data analysis software systems. See http://www.stats.ox.ac.uk/pub/MASS3/Sprog/ for more information.
In addition to material written specifically or explicitly for R, documentation for S/S-Plus (see R and S) can be used in combination with this FAQ (see What are the differences between R and S?). Introductory books include
P. Spector (1994), “An introduction to S and S-Plus”, Duxbury Press.A. Krause and M. Olsen (2005), “The Basics of S-Plus” (Fourth Edition). Springer, ISBN 0-387-26109-5.
The book
J. C. Pinheiro and D. M. Bates (2000), “Mixed-Effects Models in S and S-Plus”, Springer, ISBN 0-387-98957-0
provides a comprehensive guide to the use of the nlme package for linear and nonlinear mixed-effects models.
As an example of how R can be used in teaching an advanced introductory statistics course, see
D. Nolan and T. Speed (2000), “Stat Labs: Mathematical Statistics Through Applications”, Springer Texts in Statistics, ISBN 0-387-98974-9
This integrates theory of statistics with the practice of statistics through a collection of case studies (“labs”), and uses R to analyze the data. More information can be found at http://www.stat.Berkeley.EDU/users/statlabs/.
Last, but not least, Ross' and Robert's experience in designing and implementing R is described in Ihaka & Gentleman (1996), “R: A Language for Data Analysis and Graphics”, Journal of Computational and Graphical Statistics, 5, 299–314.
An annotated bibliography (BibTeX format) of R-related publications which includes most of the above references can be found at
http://www.R-project.org/doc/bib/R.bib
To cite R in publications, use
@Manual{,
title = {R: A Language and Environment for Statistical
Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = 2008,
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org}
}
Citation strings (or BibTeX entries) for R and R packages can also be
obtained by citation().
Thanks to Martin Maechler, there are four mailing lists devoted to R.
R-announceR-packagesR-helpR-develPlease read the posting guide before sending anything to any mailing list.
Note in particular that R-help is intended to be comprehensible to people who want to use R to solve problems but who are not necessarily interested in or knowledgeable about programming. Questions likely to prompt discussion unintelligible to non-programmers (e.g., questions involving C or C++) should go to R-devel.
Convenient access to information on these lists, subscription, and archives is provided by the web interface at http://stat.ethz.ch/mailman/listinfo/. One can also subscribe (or unsubscribe) via email, e.g. to R-help by sending ‘subscribe’ (or ‘unsubscribe’) in the body of the message (not in the subject!) to R-help-request@lists.R-project.org.
Send email to R-help@lists.R-project.org to send a message to everyone on the R-help mailing list. Subscription and posting to the other lists is done analogously, with ‘R-help’ replaced by ‘R-announce’, ‘R-packages’, and ‘R-devel’, respectively. Note that the R-announce and R-packages lists are gatewayed into R-help. Hence, you should subscribe to either of them only in case you are not subscribed to R-help.
It is recommended that you send mail to R-help rather than only to the R Core developers (who are also subscribed to the list, of course). This may save them precious time they can use for constantly improving R, and will typically also result in much quicker feedback for yourself.
Of course, in the case of bug reports it would be very helpful to have code which reliably reproduces the problem. Also, make sure that you include information on the system and version of R being used. See R Bugs for more details.
See http://www.R-project.org/mail.html for more information on the R mailing lists.
The R Core Team can be reached at R-core@lists.R-project.org for comments and reports.
Many of the R project's mailing lists are also available via Gmane, from which they can be read with a web browser, using an NNTP news reader, or via RSS feeds. See http://dir.gmane.org/index.php?prefix=gmane.comp.lang.r. for the available mailing lists, and http://www.gmane.org/rss.php for details on RSS feeds.
The “Comprehensive R Archive Network” (CRAN) is a collection of sites which carry identical material, consisting of the R distribution(s), the contributed extensions, documentation for R, and binaries.
The CRAN master site at Wirtschaftsuniversität Wien, Austria, can be found at the URL
http://CRAN.R-project.org/
Daily mirrors are available at URLs including
http://cran.at.R-project.org/ (WU Wien, Austria) http://cran.au.R-project.org/ (PlanetMirror, Australia) http://cran.br.R-project.org/ (Universidade Federal do Paraná, Brazil) http://cran.ch.R-project.org/ (ETH Zürich, Switzerland) http://cran.dk.R-project.org/ (SunSITE, Denmark) http://cran.es.R-project.org/ (Spanish National Research Network, Madrid, Spain) http://cran.fr.R-project.org/ (INRA, Toulouse, France) http://cran.pt.R-project.org/ (Universidade do Porto, Portugal) http://cran.uk.R-project.org/ (U of Bristol, United Kingdom) http://cran.za.R-project.org/ (Rhodes U, South Africa)
See http://CRAN.R-project.org/mirrors.html for a complete list of mirrors. Please use the CRAN site closest to you to reduce network load.
From CRAN, you can obtain the latest official release of R, daily snapshots of R (copies of the current source trees), as gzipped and bzipped tar files, a wealth of additional contributed code, as well as prebuilt binaries for various operating systems (Linux, Mac OS Classic, Mac OS X, and MS Windows). CRAN also provides access to documentation on R, existing mailing lists and the R Bug Tracking system.
To “submit” to CRAN, simply upload to ftp://CRAN.R-project.org/incoming/ and send an email to CRAN@R-project.org. Note that CRAN generally does not accept submissions of precompiled binaries due to security reasons. In particular, binary packages for Windows and Mac OS X are provided by the respective binary package maintainers.
Note: It is very important that you indicate the copyright (license) information (GPL-2, GPL-3, BSD, Artistic, ...) in your submission.
Please always use the URL of the master site when referring to CRAN.
R is released under the GNU General Public License (GPL). If you have any questions regarding the legality of using R in any particular situation you should bring it up with your legal counsel. We are in no position to offer legal advice.
It is the opinion of the R Core Team that one can use R for commercial purposes (e.g., in business or in consulting). The GPL, like all Open Source licenses, permits all and any use of the package. It only restricts distribution of R or of other programs containing code from R. This is made clear in clause 6 (“No Discrimination Against Fields of Endeavor”) of the Open Source Definition:
The license must not restrict anyone from making use of the program in a specific field of endeavor. For example, it may not restrict the program from being used in a business, or from being used for genetic research.
It is also explicitly stated in clause 0 of the GPL, which says in part
Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program.
Most add-on packages, including all recommended ones, also explicitly allow commercial use in this way. A few packages are restricted to “non-commercial use”; you should contact the author to clarify whether these may be used or seek the advice of your legal counsel.
None of the discussion in this section constitutes legal advice. The R Core Team does not provide legal advice under any circumstances.
The name is partly based on the (first) names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs language `S' (see What is S?).
The R Foundation is a not for profit organization working in the public interest. It was founded by the members of the R Core Team in order to provide support for the R project and other innovations in statistical computing, provide a reference point for individuals, institutions or commercial enterprises that want to support or interact with the R development community, and to hold and administer the copyright of R software and documentation. See http://www.R-project.org/foundation/ for more information.
S is a very high level language and an environment for data analysis and graphics. In 1998, the Association for Computing Machinery (ACM) presented its Software System Award to John M. Chambers, the principal designer of S, for
the S system, which has forever altered the way people analyze, visualize, and manipulate data ...S is an elegant, widely accepted, and enduring software system, with conceptual integrity, thanks to the insight, taste, and effort of John Chambers.
The evolution of the S language is characterized by four books by John Chambers and coauthors, which are also the primary references for S.
This is also referred to as the “Brown Book”, and of historical interest only.
This book is often called the “Blue Book”, and introduced what is now known as S version 2.
This is also called the “White Book”, and introduced S version 3, which added structures to facilitate statistical modeling in S.
This “Green Book” describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process.
See http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html for further information on “Stages in the Evolution of S”.
There is a huge amount of user-contributed code for S, available at the S Repository at CMU.
S-Plus is a value-added version of S sold by Insightful Corporation. Based on the S language, S-Plus provides functionality in a wide variety of areas, including robust regression, modern non-parametric regression, time series, survival analysis, multivariate analysis, classical statistical tests, quality control, and graphics drivers. Add-on modules add additional capabilities.
See the Insightful S-Plus page for further information.
We can regard S as a language with three current implementations or “engines”, the “old S engine” (S version 3; S-Plus 3.x and 4.x), the “new S engine” (S version 4; S-Plus 5.x and above), and R. Given this understanding, asking for “the differences between R and S” really amounts to asking for the specifics of the R implementation of the S language, i.e., the difference between the R and S engines.
For the remainder of this section, “S” refers to the S engines and not the S language.
Contrary to other implementations of the S language, R has adopted an evaluation model in which nested function definitions are lexically scoped. This is analogous to the evalutation model in Scheme.
This difference becomes manifest when free variables occur in a function. Free variables are those which are neither formal parameters (occurring in the argument list of the function) nor local variables (created by assigning to them in the body of the function). In S, the values of free variables are determined by a set of global variables (similar to C, there is only local and global scope). In R, they are determined by the environment in which the function was created.
Consider the following function:
cube <- function(n) {
sq <- function() n * n
n * sq()
}
Under S, sq() does not “know” about the variable n
unless it is defined globally:
S> cube(2)
Error in sq(): Object "n" not found
Dumped
S> n <- 3
S> cube(2)
[1] 18
In R, the “environment” created when cube() was invoked is
also looked in:
R> cube(2)
[1] 8
As a more “interesting” real-world problem, suppose you want to write a function which returns the density function of the r-th order statistic from a sample of size n from a (continuous) distribution. For simplicity, we shall use both the cdf and pdf of the distribution as explicit arguments. (Example compiled from various postings by Luke Tierney.)
The S-Plus documentation for call() basically suggests the
following:
dorder <- function(n, r, pfun, dfun) {
f <- function(x) NULL
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
PF <- call(substitute(pfun), as.name("x"))
DF <- call(substitute(dfun), as.name("x"))
f[[length(f)]] <-
call("*", con,
call("*", call("^", PF, r - 1),
call("*", call("^", call("-", 1, PF), n - r),
DF)))
f
}
Rather tricky, isn't it? The code uses the fact that in S, functions are just lists of special mode with the function body as the last argument, and hence does not work in R (one could make the idea work, though).
A version which makes heavy use of substitute() and seems to work
under both S and R is
dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
eval(substitute(function(x) K * PF(x)^a * (1 - PF(x))^b * DF(x),
list(PF = substitute(pfun), DF = substitute(dfun),
a = r - 1, b = n - r, K = con)))
}
(the eval() is not needed in S).
However, in R there is a much easier solution:
dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
function(x) {
con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
}
}
This seems to be the “natural” implementation, and it works because the free variables in the returned function can be looked up in the defining environment (this is lexical scope).
Note that what you really need is the function closure, i.e., the
body along with all variable bindings needed for evaluating it. Since
in the above version, the free variables in the value function are not
modified, you can actually use it in S as well if you abstract out the
closure operation into a function MC() (for “make closure”):
dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
MC(function(x) {
con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
},
list(con = con, pfun = pfun, dfun = dfun, r = r, n = n))
}
Given the appropriate definitions of the closure operator, this works in both R and S, and is much “cleaner” than a substitute/eval solution (or one which overrules the default scoping rules by using explicit access to evaluation frames, as is of course possible in both R and S).
For R, MC() simply is
MC <- function(f, env) f
(lexical scope!), a version for S is
MC <- function(f, env = NULL) {
env <- as.list(env)
if (mode(f) != "function")
stop(paste("not a function:", f))
if (length(env) > 0 && any(names(env) == ""))
stop(paste("not all arguments are named:", env))
fargs <- if(length(f) > 1) f[1:(length(f) - 1)] else NULL
fargs <- c(fargs, env)
if (any(duplicated(names(fargs))))
stop(paste("duplicated arguments:", paste(names(fargs)),
collapse = ", "))
fbody <- f[length(f)]
cf <- c(fargs, fbody)
mode(cf) <- "function"
return(cf)
}
Similarly, most optimization (or zero-finding) routines need some arguments to be optimized over and have other parameters that depend on the data but are fixed with respect to optimization. With R scoping rules, this is a trivial problem; simply make up the function with the required definitions in the same environment and scoping takes care of it. With S, one solution is to add an extra parameter to the function and to the optimizer to pass in these extras, which however can only work if the optimizer supports this.
Nested lexically scoped functions allow using function closures and maintaining local state. A simple example (taken from Abelson and Sussman) is obtained by typing demo("scoping") at the R prompt. Further information is provided in the standard R reference “R: A Language for Data Analysis and Graphics” (see What documentation exists for R?) and in Robert Gentleman and Ross Ihaka (2000), “Lexical Scope and Statistical Computing”, Journal of Computational and Graphical Statistics, 9, 491–508.
Nested lexically scoped functions also imply a further major difference. Whereas S stores all objects as separate files in a directory somewhere (usually .Data under the current directory), R does not. All objects in R are stored internally. When R is started up it grabs a piece of memory and uses it to store the objects. R performs its own memory management of this piece of memory, growing and shrinking its size as needed. Having everything in memory is necessary because it is not really possible to externally maintain all relevant “environments” of symbol/value pairs. This difference also seems to make R faster than S.
The down side is that if R crashes you will lose all the work for the
current session. Saving and restoring the memory “images” (the
functions and data stored in R's internal memory at any time) can be a
bit slow, especially if they are big. In S this does not happen,
because everything is saved in disk files and if you crash nothing is
likely to happen to them. (In fact, one might conjecture that the S
developers felt that the price of changing their approach to persistent
storage just to accommodate lexical scope was far too expensive.)
Hence, when doing important work, you might consider saving often (see
How can I save my workspace?) to safeguard against possible
crashes. Other possibilities are logging your sessions, or have your R
commands stored in text files which can be read in using
source().
Note: If you run R from within Emacs (see R and Emacs), you can save the
contents of the interaction buffer to a file and conveniently manipulate
it using ess-transcript-mode, as well as save source copies of
all functions and data used.
There are some differences in the modeling code, such as
lm(y ~ x^3) to regress y on
x^3, in R, you have to insulate powers of numeric vectors (using
I()), i.e., you have to use lm(y ~ I(x^3)).
na.action is set to "na.omit" by default in R,
but not set in S.
y ~ x + 0 is an alternative to y ~ x - 1 for
specifying a model with no intercept. Models with no parameters at all
can be specified by y ~ 0.
Apart from lexical scoping and its implications, R follows the S language definition in the Blue and White Books as much as possible, and hence really is an “implementation” of S. There are some intentional differences where the behavior of S is considered “not clean”. In general, the rationale is that R should help you detect programming errors, while at the same time being as compatible as possible with S.
Some known differences are the following.
x is a list, then x[i] <- NULL and x[[i]]
<- NULL remove the specified elements from x. The first of
these is incompatible with S, where it is a no-op. (Note that you can
set elements to NULL using x[i] <- list(NULL).)
.First and .Last in the
.Data directory can be used for customizing, as they are executed
at the very beginning and end of a session, respectively.
In R, the startup mechanism is as follows. Unless --no-environ
was given on the command line, R searches for site and user files to
process for setting environment variables. Then, R searches for a
site-wide startup profile unless the command line option
--no-site-file was given. This code is loaded in package
base. Then, unless --no-init-file was given, R
searches for a user profile file, and sources it into the user
workspace. It then loads a saved image of the user workspace from
.RData in case there is one (unless --no-restore-data or
--no-restore were specified). Next, a function .First()
is run if found on the search path. Finally, function .First.sys
in the base package is run. When terminating an R session, by
default a function .Last is run if found on the search path,
followed by .Last.sys. If needed, the functions .First()
and .Last() should be defined in the appropriate startup
profiles. See the help pages for .First and .Last for
more details.
T and F are just variables being set to TRUE
and FALSE, respectively, but are not reserved words as in S and
hence can be overwritten by the user. (This helps e.g. when you have
factors with levels "T" or "F".) Hence, when writing code
you should always use TRUE and FALSE.
dyn.load() can only load shared objects, as created
for example by R CMD SHLIB.
attach() currently only works for lists and data frames,
but not for directories. (In fact, attach() also works for R
data files created with save(), which is analogous to attaching
directories in S.) Also, you cannot attach at position 1.
For() loops are not necessary and hence not supported.
assign() uses the argument envir= rather than
where= as in S.
int * rather than long *
as in S.
ls() returns the names of the objects in the current
(under R) and global (under S) environment, respectively. For example,
given
x <- 1; fun <- function() {y <- 1; ls()}
then fun() returns "y" in R and "x" (together with
the rest of the global environment) in S.
dim attribute vector can be 0). This has been determined a
useful feature as it helps reducing the need for special-case tests for
empty subsets. For example, if x is a matrix, x[, FALSE]
is not NULL but a “matrix” with 0 columns. Hence, such objects
need to be tested for by checking whether their length() is zero
(which works in both R and S), and not using is.null().
is.vector(c(a = 1:3)) returns FALSE in S and TRUE
in R).
DF is a
data frame, then is.matrix(DF) returns FALSE in R and
TRUE in S).
f(a) <- b is
evaluated as a <- "f<-"(a, value = b). S always takes the last
argument, irrespective of its name.
substitute() searches for names for substitution in the
given expression in three places: the actual and the default arguments
of the matching call, and the local frame (in that order). R looks in
the local frame only, with the special rule to use a “promise” if a
variable is not evaluated. Since the local frame is initialized with
the actual arguments or the default expressions, this is usually
equivalent to S, until assignment takes place.
for() loop is local to the inside
of the loop. In R it is local to the environment where the for()
statement is executed.
tapply(simplify=TRUE) returns a vector where R returns a
one-dimensional array (which can have named dimnames).
"aA" < "Bb" is
true or false). From version 1.2.0 the locale can be (re-)set in R by
the Sys.setlocale() function.
missing(arg) remains TRUE if arg is
subsequently modified; in R it doesn't.
data.frame strips I() when creating
(column) names.
"NA" is not treated as a missing value in a
character variable. Use as.character(NA) to create a missing
character value.
dump(), dput() and deparse() are essentially
different interfaces to the same code. In R from version 2.0.0, this is
only true if the same control argument is used, but by default it
is not. By default dump() tries to write code that will evaluate
to reproduce the object, whereas dput() and deparse()
default to options for producing deparsed code that is readable.
[ using
a character vector index looks only for exact matches (whereas [[
and $ allow partial matches). In S, [ allows partial
matches.
atan and no atan2. A call
in S such as atan(x1, x2) is equivalent to R's atan2(x1,
x2). However, beware of named arguments since S's atan(x = a, y
= b) is equivalent to R's atan2(y = a, x = b) with the meanings
of x and y interchanged. (R used to have undocumented
support for a two-argument atan with positional arguments, but
this has been withdrawn to avoid further confusion.)
There are also differences which are not intentional, and result from missing or incorrect code in R. The developers would appreciate hearing about any deficiencies you may find (in a written report fully documenting the difference as you see it). Of course, it would be useful if you were to implement the change yourself and make sure it works.
Since almost anything you can do in R has source code that you could port to S-Plus with little effort there will never be much you can do in R that you couldn't do in S-Plus if you wanted to. (Note that using lexical scoping may simplify matters considerably, though.)
R offers several graphics features that S-Plus does not, such as finer
handling of line types, more convenient color handling (via palettes),
gamma correction for color, and, most importantly, mathematical
annotation in plot texts, via input expressions reminiscent of TeX
constructs. See the help page for plotmath, which features an
impressive on-line example. More details can be found in Paul Murrell
and Ross Ihaka (2000), “An Approach to Providing Mathematical
Annotation in Plots”, Journal of Computational and Graphical Statistics, 9,
582–599.
For a very long time, there was no such thing.
XLSolutions Corporation is currently beta testing a commercially supported version of R named R+ (read R plus).
In addition, REvolution Computing has released RPro, an enterprise-class statistical analysis system based on R, suitable for deployment in professional, commercial and regulated environments.
Random Technologies offers RStat, an enterprise-strength statistical computing environment which combines R with enterprise-level validation, documentation, software support, and consulting services, as well as related R-based products.
Rweb is developed and maintained by Jeff Banfield. The Rweb Home Page provides access to all three versions of Rweb—a simple text entry form that returns output and graphs, a more sophisticated Javascript version that provides a multiple window environment, and a set of point and click modules that are useful for introductory statistics courses and require no knowledge of the R language. All of the Rweb versions can analyze Web accessible datasets if a URL is provided.
The paper “Rweb: Web-based Statistical Analysis”, providing a detailed explanation of the different versions of Rweb and an overview of how Rweb works, was published in the Journal of Statistical Software (http://www.jstatsoft.org/v04/i01/).
Ulf Bartel has developed R-Online, a simple on-line programming environment for R which intends to make the first steps in statistical programming with R (especially with time series) as easy as possible. There is no need for a local installation since the only requirement for the user is a JavaScript capable browser. See http://osvisions.com/r-online/ for more information.
Rcgi is a CGI WWW interface to R by MJ Ray. It had the ability to use “embedded code”: you could mix user input and code, allowing the HTML author to do anything from load in data sets to enter most of the commands for users without writing CGI scripts. Graphical output was possible in PostScript or GIF formats and the executed code was presented to the user for revision. However, it is not clear if the project is still active. Currently, a modified version of Rcgi by Mai Zhou (actually, two versions: one with (bitmap) graphics and one without) as well as the original code are available from http://www.ms.uky.edu/~statweb/.
CGI-based web access to R is also provided at http://hermes.sdu.dk/cgi-bin/go/. There are many additional examples of web interfaces to R which basically allow to submit R code to a remote server, see for example the collection of links available from http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/StatCompCourse.
David Firth has written CGIwithR, an R add-on package available from CRAN. It provides some simple extensions to R to facilitate running R scripts through the CGI interface to a web server, and allows submission of data using both GET and POST methods. It is easily installed using Apache under Linux and in principle should run on any platform that supports R and a web server provided that the installer has the necessary security permissions. David's paper “CGIwithR: Facilities for Processing Web Forms Using R” was published in the Journal of Statistical Software (http://www.jstatsoft.org/v08/i10/). The package is now maintained by Duncan Temple Lang and has a web page at http://www.omegahat.org/CGIwithR/.
Rpad, developed and actively maintained by Tom Short, provides a sophisticated environment which combines some of the features of the previous approaches with quite a bit of Javascript, allowing for a GUI-like behavior (with sortable tables, clickable graphics, editable output), etc.
Jeff Horner is working on the R/Apache Integration Project which embeds the R interpreter inside Apache 2 (and beyond). A tutorial and presentation are available from the project web page at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RApacheProject.
Rserve is a project actively developed by Simon Urbanek. It implements a TCP/IP server which allows other programs to use facilities of R. Clients are available from the web site for Java and C++ (and could be written for other languages that support TCP/IP sockets).
OpenStatServer is being developed by a team lead by Greg Warnes; it aims “to provide clean access to computational modules defined in a variety of computational environments (R, SAS, Matlab, etc) via a single well-defined client interface” and to turn computational services into web services.
Two projects use PHP to provide a web interface to R. R_PHP_Online by Steve Chen (though it is unclear if this project is still active) is somewhat similar to the above Rcgi and Rweb. R-php is actively developed by Alfredo Pontillo and Angelo Mineo and provides both a web interface to R and a set of pre-specified analyses that need no R code input.
webbioc is “an integrated web interface for doing microarray analysis using several of the Bioconductor packages” and is designed to be installed at local sites as a shared computing resource.
Finally, Rwui is a web application to to create user-friendly web interfaces for R scripts. All code for the web interface is created automatically. There is no need for the user to do any extra scripting or learn any new scripting techniques.
The R distribution comes with the following packages:
The following packages are available from the CRAN src/contrib area. (Packages denoted as Recommended are to be included in all binary distributions of R.)